Panel 1 - Activity Status Log
Panel 2 - Real-time Noise Exposure
Panel 3 - File Management
AWS Cloud Repository
Panel 4 - Settings
Panel 5 - Noise Exposure Health Issues
Panel 6 - Permissible Noise Exposure Times
Common Decibel Levels
Every day we are exposed to harmful levels of noise and don't even realize it. Studies show the adverse impact of sustained or prolonged loud noise on our health is not just limited to hearing impairment, but can include increased risk of hypertension, heart disease and stress. Annoyance, sleep disturbance and decreased school performance are also sufficiently attributed to noise exposure (ref: Passchier-Vermeer W, Passchier WF (2000). "Noise exposure and public health". Environ. Health Perspect. 108 (Suppl 1): 123–31. doi:10.2307/3454637).
Additionally, in occupational settings there are tools that have been developed delineating high-risk noise exposure zones based on point source measurement, but there is a need for a cost effective personal noise exposure capture device used for data collection and visualization in real-time. Deployment of ten or a dozen NEATVIBEwear devices in a single factory/plant production area is more cost effective than procurement of one industrial noise monitoring handheld device.
To reliably assess exposure to occupational risk factors it is vital to have estimates of where workers spend their time and how they spend it. The study of Time Activity Patterns (TAP) attempts to answers these questions. Most time-activity studies for health data collection are written in diaries or entered in on-line surveys, which are inherently biased due to differing degrees of recall and accuracy in reporting (ref: Time Activity Patterns in Exposure Assessment, EUR 15892 Report, EC Directorate on Science., R&D, 1995).
Some TAP studies now can be mobile device enabled, but few (if any) record real-time noise exposure coupled with time-activity data. Fitness trackers have similar functionality, but do not include noise exposure. Collection of accurate, cost-effective real-time TAP and noise exposure would enable factory or plant workers to better protect themselves from risk of hearing loss and other noise-associated harms as studies show work-induced hearing loss is a result of repetitive high noise exposure and workers who are overexposed to noise are at risk for developing hypertension (ref: Assessing High Noise Areas, G.Battista, OH&S, May 2016).
The National Institute of Occupational Safety and Health (NIOSH) within the Center for Disease Control (CDC) has established exposure limits for various decibel (dB) ranges, and yet most people aren't aware of their exposure to noise nor exposure at various noise levels.
Although there are smart phone apps that can measure dB levels, these apps are limited as they can produce false and/or interrupted readings resulting from phone calls, texts and/or when the app is closed. Additionally, the Smartphone sampling rates may be constrained and battery life can be a concern as well. Finally, if the Smartphone is off it can't capture noise level information.
Hence, a Noise Exposure, Activity-Time and Vibration Wearable (NEATVIBEwear) device!!
What NEATVIBEwear does
NEATVIBEwear is a personal noise capture device integrated to a Smartphone (for the purpose of displaying information) giving users the ability to:
- View real-time noise exposure at a glance
- Monitor and track cumulative noise exposure either on the NIOSH scale or broader "Health Risk" scale
- Save summary exposure information to log noise cumulative exposure over time
- Review and understand available information regarding noise exposure
How we built it
NEATVIBEwear is an integration between a personal noise capture device built using an Arduino 101 with the Grove Loudness Sensor (with the potentiometer for adjusting gain and a wide frequency range) and Android Smartphone running the gaming platform Unity as it provides the ability to render a representation of the real-time sound in a 3D color coded simulation. The Arduino 101 and the Smartphone are integrated through the Arduino 101's on-board Curie Bluetooth Low Energy (BLE) capability.
We use the standard formula (20*log10(V/Vo) to convert the electrical signal from the loudness sensor to dBs and then use additional mapping and smoothing calculations to calibrate the dB readings to external digital sound level meter (SLM) readings. This approach is ideal and practical for showing both NIOSH and "Health Risk" scales of noise levels of exposure.
Challenges we ran into
Working with new technology always presents challenges and we can't think of anything that wasn’t associated to some challenge. Below are the major challenges we encountered:
- Understanding the BLE architecture and how to use it from publish-subscribe APIs to UUIDs for the services and characteristics.
- Learning how to manage the BLE states and hex data conversion within the Unity C# scripts.
- Figuring out how to convert the loudness sensor electrical signal output to accurate dB readings across a range of noise frequencies to display on the mobile app including use of signal to dB conversion, equations for mapping and smoothing and a SLM for refined calibration.
- Realizing our expected use for the Grove Vibration Sensor was not in align with the intended use for the sensor resulting in a re-directing of our focus toward the more important noise monitoring and tracking and removal of the scope associated with the vibration and motion sensing. We learned that our original expectation for the vibration sensor based on web findings, for instance, to detect and quantify the low frequency rumble of a motor or vibration imparted to the NEATVIBEwear device was just not correct.
Accomplishments that we're proud of
- Having developed a product to help people understand their own personal noise exposure and more importantly their cumulative exposure at various noise levels which is an important step to reduce their noise exposure and associated risks to their health.
- Creating a product with a framework for capturing noise data for further research and studies.
- Deciding to use the Smartphone as only a display device reducing the risks of bad data and/or insufficiently granular data.
- Selecting Unity as the rendering and display environment as it opens up opportunities to integrate real-time sensor data easily with a multitude of delivery platforms including: mobile, PC, browser and as well Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR).
- Successfully integrating components together that we had never used or integrated together before in a very short time period.
What we learned
- Taking on challenges with only little to no experience can be exceptionally rewarding, exciting and fun! One never knows what one can learn!
- Using emerging technologies have the potential to define opportunities in the future with just a bit of creativity.
- Working with the Arduino 101 and the Grove Starter Kit Plus on the hack opened up our eyes to how exciting these products are as we have only used a couple of the sensors. We are now Arduino 101 and Grove sensor evangelists!
- Stopping work on this idea is not an option as we now see the important value NEATVIBEwear can have reducing health risks to individuals and society in addition to providing better consistency and quality data to research and studies, for example, crowd sourcing data from multiple NEATVIBEwear users within a noise-impacted area.
What's next for NEATVIBEwear
We are currently planning to:
- Enhance the functionality and capability to support not only a better user interface, but additionally to educate users that they are not just helping themselves reduce health risks, but they are "Citizen Scientists" providing important information for research and studies to reduce health risks throughout society.
- Improve the quality and accuracy of the data calculations to increase the value of the information captured for research and studies. We used a fairly simplistic approach to meet the hack objective to show the exposure for both NIOSH and "Health Risk" noise level scales. More precise calculations would be necessary to support research and study needs.
- Work with companies and institutions (e.g. - manufacturing plants, hospitals, construction companies, etc.) who expose their employees to high noise levels and redesign this product to help employers understand those exposure levels as well as reduce the health risks associated to noise exposure to better protect the health of their employees.
- Add capability to save detailed data on the Arduino coupled with the functionality to send that detailed data to the cloud to better support research and studies in addition to IOT opportunities and initiatives.